An Integrative Computational Approach for Identifying Cotton Host Plant MicroRNAs with Potential to Abate CLCuKoV-Bur...

Area of Science:

• Plant Virology
• Molecular Biology
• Bioinformatics

Background:

• Cotton leaf curl disease (CLCuD), caused by Cotton leaf curl Kokhran virus (CLCuKoV-Bur), is a significant threat to cotton production.
• CLCuKoV-Bur is a single-stranded DNA virus belonging to the genus Begomovirus, transmitted by the whitefly Bemisia tabaci.
• RNA interference (RNAi) is a natural gene regulation process with potential for antiviral strategies.

Purpose of the Study:

• To investigate the potential of cotton-genome-encoded microRNAs (ghr-miRNAs) to target CLCuKoV-Bur mRNAs.
• To identify specific ghr-miRNAs that can be utilized for developing CLCuD resistance in upland cotton.

Main Methods:

• Utilized four online bioinformatics tools (RNA22, psRNATarget, RNAhybrid, TAPIR) for in silico prediction of miRNA binding sites.
• Selected 80 experimentally validated cotton miRNAs from miRBase (v22).
• In silico hybridization of selected ghr-miRNAs against the CLCuKoV-Bur genome (AM421522) coding and non-coding sequences.

Main Results:

• Identified 18 ghr-miRNAs that showed binding potential to CLCuKoV-Bur sequences, validated by two to four prediction algorithms.
• The ghr-miR399d was consistently predicted by all four algorithms as an optimal target within the CLCuKoV-Bur genome.
• Developed an in silico ghr-miRNA-regulatory network for CLCuKoV-Bur open reading frames using Circos software.

Conclusions:

• These findings represent the first prediction of ghr-miRNAs with potential therapeutic applications against CLCuD.
• The identified ghr-miRNAs, particularly ghr-miR399d, can serve as a basis for designing artificial miRNA (amiRNA) silencing constructs.
• This study provides a foundation for developing novel RNAi-based strategies to enhance CLCuD resistance in upland cotton.